• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.333-336
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• 1991

In this paper, Hopfield & Tank model-like artificial neural network structure is proposed, which can be used for the optimal path planning problems such as the unit commitment problems or the maintenance scheduling problems which have been solved by the dynamic programming method or the branch and bound method. To construct the structure of the neural network, an energy function is defined, of which the global minimum means the optimal path of the problem. To avoid falling into one of the local minima during the optimization process, the simulated annealing method is applied via making the slope of the sigmoid transfer functions steeper gradually while the process progresses. As a result, computer(IBM 386-AT 34MHz) simulations can finish the optimal unit commitment problem with 10 power units and 24 hour periods (1 hour factor) in 5 minites. Furthermore, if the full parallel neural network hardware is contructed, the optimization time will be reduced remarkably.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.311-321
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• 2005

A competitive generating company (GENCO) could maximize its payoff by optimizing its generation assets. This paper considers the GENCO's arbitrage problem using price-based unit commitment (PBUC). The GENCO could consider arbitrage opportunities in purchases from qualifying facilities (QFs) as well as simultaneous trades with spots markets for energy, ancillary services, emission, and fuel. Given forecasted hourly market prices for each market, the GENCO's generating asset arbitrage problem is formulated as a mixed integer program (MIP) and solved by a branch-and-cut algorithm. A GENCO with 54 thermal and 12 combined-cycle units is considered for analyzing the proposed formulation. The proposed case studies illustrate the significance of simultaneous arbitrage by applying PBUC to multi-commodity markets.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.10
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• pp.675-682
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• 1988

An improved algorithm for optimal daily hydrothermal unit commitment is proposed in this paper. Especially, for the purpose of guarantee on the existence of feasible solution all the time, the secondary states as well as the primary state are considered at each step in the procedure of solution by Hybird Dynamic Programming to solve the problem of which solution may not exist due to the increased number of constraints to be taken into account. The computational problem which may be caused in the course of considering secondary policies through whole steps of the period examined is solved by the heuristic judgement in every step taking load variation rates into account. The effectivness of the algorithm has been demonstrated by applying it to a sample power system.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1003-1005
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• 1998

We present the implementation of a Lagrangian Relaxation based large scale thermal Unit Commitment problem. The problem is decomposed into thermal subproblem by using Lagrangian multipliers. The thermal subproblem is solved by using dynamic programmmg. we perform a numerical test using the thermal system of KEPCO over a week (168 hours) period. The programming language used for the test program is C. The result is compared with the priority list method.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2187-2195
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• 2017

In order to solve the AC optimal power flow (OPF) problem considering the generators' on/off status, it is necessary to model the problem as mixed-integer nonlinear programming (MINLP). Because the computation time to find the optimal solution to the mixed-integer AC OPF problem increases significantly as the system becomes larger, most of the existing solutions simplify the problem either by deciding the on/off status of generators using a separate unit commitment algorithm or by ignoring the minimum output of the generators. Even though this kind of simplification may make the overall computation time tractable, the results can be significantly erroneous. This paper proposes a novel algorithm for the mixed-integer AC OPF problem, which can provide a near-optimal solution quickly and efficiently. The proposed method is based on a combination of the outer approximation method and the relaxed AC OPF theory. The method is applied to a real-scale power system that has 457 generators and 2132 buses, and the result is compared to the branch-and-bound (B&B) method and the genetic algorithm. The results of the proposed method are almost identical to those of the compared methods, but computation time is significantly shorter.

• Journal of the Korean Operations Research and Management Science Society
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• v.15 no.1
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• pp.99-115
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• 1990

This paper is concerned with the unit commitment problem in an electric power system with both thermal and pumped-storage hydroelectric units. This is a mixed integer programming problem and the Lagrangean relaxation method is used. We show that the relaxed problem decomposes into two kinds of subproblems : a shortest-path problem for each thermal unit and a minimum cost flow problem for each pumped-storage hydroelectric unit. A method of obtaining an incumbenet solution from the solution of a relaxed problem is presented. The Lagrangean multipliers are updated using both subgradient and incremental cost. The algorithm is applied to a real Korean power generation system and its computational results are reported and compaired with other works.

• 전기의세계
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• v.21 no.2
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• pp.34-40
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• 1972

This paper describes the problem of the shortange-economic-scheduling for unit commitment and load dispatching in thermal power system. For economic operation of thermal system, the optimum time of startup and shoutdown of the generating unit must be determined so as to minimize the sum of generating and starting fuel cost over a given period. The above problems are analyzed for the purpose of the application of Dynamic Programing Method. Also the technique of Dynamic Programming is applied to the problems. For the illustative purpose, a case study was made on a model system composed of eight units and the computing time was about 190 seconds by IBM 360-40 system. Therefore, one can utilize this suggested method on any of the practical power systems.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.174-176
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• 1997

This paper proposes a unit commitment scheduling method based on hybrid genetic algorithm(GA). When the systems are scaled up, conventional genetic algorithms suffer from computational time limitations because of the growth of the search space. So greatly reduce the search space of the GA and to efficiently deal with the constraints of the problem, priority list unit ordering scheme are incorporated as the initial solution and the minimum up and down time constraints of the units are included. The violations of other constraints are handled by integrating penalty factors. To show the effectiveness of the proposed method. test results for system of 10 units is compared with results obtained using other methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.457-466
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• 2013

Wind energy is rapidly becoming significant generating technologies in electricity markets. As probabilistic nature of wind energy creates many uncertainties in the short-term scheduling, additional actions for reliable market operation should be taken. This paper presents a novel approach to evaluate ramping capability requirement for changes in imbalance energy between day-ahead market and real-time market due to uncertainty of wind generation as well as system load. Dynamic ramp rate model has been applied for realistic solution in unit commitment problem, which is implemented in day-ahead market. Probabilistic optimal power flow has been used to verify ramping capability determined by the proposed method is reasonable in economic and reliable aspects. This approach was tested on six-bus system and IEEE 118-bus system with a wind farm. The results show that the proposed approach provides ramping capability information to meet both forecasted variability and desired confidence level of anticipated uncertainty.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1596-1604
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• 2013

Coordinating operation between large-scale wind power and thermal units in multiple time scale is an important problem to keep power balance, especially for the power grids mainly made up of large coal-fired units. The paper proposes a novel operation mode of multi-scale unit commitment (abbr. UC) that includes mid-term UC and day-ahead UC, which can take full advantage of insufficient flexibility and improve wind power accommodation. First, we introduce the concepts of multi-scale UC and then illustrate the benefits of introducing mid-term UC to the wind-coal intensive grid. The paper then formulates the mid-term UC model, proposes operation performance indices and validates the optimal operation mode by simulation cases. Compared with day-ahead UC only, the multi-scale UC mode could reduce the total generation cost and improve the wind power net benefit by decreasing the coal-fired units' on/off operation. The simulation results also show that the maximum total generation benefit should be pursued rather than the wind power utilization rate in wind-coal intensive system.